Robotic control has found many applications over the past few decades. No longer are robots limited to the laboratory or fixed in place for manufacturing products in factories. Robots and robotic control now include terrain assessment and terrain mapping for use in military, space exploration, and automotive applications. A mobile robot may be sent into unknown off-road terrain, such as jungles, deserts, and hills, where generally smooth, flat terrain is rare. It would be desirable for such a robot to quickly adapt to the new terrain and to explore and map the terrain without human tuning or training.
In automotive applications, unmanned autonomous vehicles may be configured to follow paths or roads, especially when lane markings are absent, to patrol small roads or navigate paths with an internal map. If a vehicle encounters a particular type of terrain, it may be able to predict the nature of the terrain several yards in advance. Thus, an autonomous vehicle design may be configured to steer itself.
Yet a third application is slip detection. Manned and unmanned vehicles need to recognize and avoid poor driving surfaces such as loose sand or soft mud. The appearance of such surfaces may change dramatically and, as a result, fool a human driver or a simple visual recognition system into making unforeseen and potentially dangerous mistakes.
All such robot and autonomous vehicle control and navigation applications are dependent on both short and long range accurate terrain recognition. The design of such systems should meet certain goals: the navigation system should be robust to irrelevant variations and occlusions, be adaptive to new environments, and adapt in real time or near real time.
Unfortunately, prior art terrain detection and navigation systems have been poor in assessing the kind of large environmental variations found in extreme environments, such as deserts, marshes, and jungles. Prior art navigation systems have been poor in adapting in real time to dynamic changes in appearance, such as lighting conditions, occlusions, and shape variability. Further, long range 2D or 3D sensors have been poor at assessing surface qualities such as hardness, slip, and/or traction. At best, prior art terrain detection systems have employed classification of objects or terrain into a limited list of categories. As a result, such system cannot adapt to objects not found in the list.
Accordingly, what would be desirable, but has not yet been provided, is a system and method for real-time or near-real-time automatic, unattended detection of short and long distance terrain for accurate control and navigation of mobile robots and autonomous vehicles.